Radio wave resources are limited national intangible assets, and the use of wireless devices is sharply increasing. Accordingly, the value of the radio wave resources is gradually increasing. In particular, as services using a wireless technology sharply increase, such as radio frequency identification/ubiquitous sensor networks (RFID/USN) and ultra wide band (UWB) communication and wireless broadband (WiBro) as well as mobile communication, wireless LAN, digital broadcasting, and satellite communication, demand for the limited radio wave resources is persistently increasing.
As such, limited frequency resources must be shared between different systems in an environment where different kinds of communication networks are distributed in the same region, giving rise to the problem of interference. Therefore, interference management is needed to solve this problem, for which cognitive radio (CR) and cooperative communication are powerfully emerging as a base technology.
As such, in order to use these important radio wave resources efficiently, developed countries including US develop technologies under the leadership of governments and actively are pushing ahead activities to establish radio wave policies on based on the technologies.
While radio wave policies in the past were based on command and control in such a manner that governments established and managed policies, it is predicted that radio wave policies in the future will be diverted to open spectrum policies.
Regarding this, the cognitive radio (CR) technology is an idea that has developed a software defined radio technology (SDR) to enhance the use efficiency of spectrum, and has been first suggested by “Joseph Mitola III”. The listen before talk (LBT) of RFID or the dynamic frequency selection (DFS) of WLAN, etc. that have been suggested so far in some fields correspond to cognitive radio of the beginning level, and systematic establishment on that has been made by the completion of thesis by “Mitola III”.
The cognitive RF technology may be said to be an intelligent RF technology that wireless terminals automatically perform optimal communication through learning and adaptation to surrounding communication environments.
FIG. 1 is an exemplary diagram illustrating a cognitive RF technology.
As seen with reference to FIG. 1, the terminal 11 of a primary user (PU) has a license for the corresponding spectrum of RF resources and a priority when using the corresponding spectrum. The occupancy of RF resources by the primary user dynamically arises in a space or time dimension.
At this point, the cognitive RF technology enables a spectrum overlay access scheme in which the terminal 12 of a secondary user finds and uses an empty resource not used by the primary user through spectrum sensing.
The secondary secondary user has become to be able to use the spectrum of the primary user through the spectrum overlay. Using the spectrum of the primary user by the secondary user was difficult in the related art.
However, since the secondary user is a user not permitted to the RF resources, he/she must find and use only the spectrum not used by the primary user and must yield the use of the spectrum even during transmission of data if the primary user attempts to the corresponding spectrum. For this reason, the secondary user must sense the corresponding spectrum, regularly.
Examples of ways of implementing the spectrum sensing include matched filter detection, feature detection, and energy detection.
The matched filter detection and feature detection must previously know primary user signal information. However, it is difficult to previously know the primary user signal information, a reason for which is as follows. First, even in an environment where the map information of TV band database (TVDB) representing the use band of TV white space (TVWS) may be obtained, non-standard equipment, such as an RF microphone may be used. In addition, since different kinds of unlicensed instruments may be simultaneously used in the TVWS such as an environment using the standard IEEE 802.19, signal features may not be known.
Thus, the energy detection is generally used.
The performance of the spectrum sensing including energy detection is greatly influenced by the signal to noise ratio (SNR) of the received primary user signal. Such influence mainly results from shadowing due to a tomographic change and fading according to a multiple path feature. Typical problems due to the shadowing and fading may include a hidden terminal problem. If the hidden terminal problem occurs, the transmission by the primary user is hindered since the secondary user transmits while the signal for the primary user has not been detected.